AI Solutions Engineering & Transformation Manager

About Morningstar
Morningstar, Inc. is a leading provider of independent investment insights in North America, Europe, Australia, and Asia. The Company offers an extensive line of products and services for individual investors, financial advisors, asset managers and owners, retirement plan providers and sponsors, institutional investors in the debt and private capital markets, and alliances and redistributors.
Morningstar provides data and research insights on a wide range of investment offerings, including managed investment products, publicly listed companies, private capital markets, debt securities, and real-time global market data.
Role Context

India GIH is scaling AI adoption across business, operations, research, technology and enabling functions. The organization already has AI awareness, AI literacy, AI Champion and showcase mechanisms in place. The next stage of maturity requires stronger execution support: teams need help converting use-case ideas into practical solution designs, prototypes, MVPs and production-ready implementations. This need is especially acute for smaller or non-technology teams that understand their workflows but do not have dedicated engineering or AI solution-design capacity.

This role is intended to fill that gap. The Manager – AI Solutions Engineering and Transformation will act as an internal AI advisor, solution architect, prototype, and solution developer & delivery catalyst. The role will not merely evangelize AI or run training sessions; it will help teams make concrete progress from idea identification to impact realization. The manager will bring consulting discipline, technical fluency, hands-on experimentation, stakeholder management and responsible AI governance into one integrated role.

Roles and Responsibilities

A. AI Advisory and Business Problem Framing

• Partner with functional leaders, managers and AI Champions to identify workflow pain points, productivity opportunities, quality challenges, turnaround-time bottlenecks and client-experience improvement areas that could benefit from AI.
• Challenge vague AI ideas and convert them into outcome-oriented use cases with clear scope, and measurable value
• Help business sponsors articulate success measures such as hours saved, effort reduction, throughput improvement, accuracy improvement, faster cycle time, reduced rework or improved user satisfaction.

B. Solution Design and Architecture Advisory

• Translate business requirements into practical AI solution designs, including user flow, data flow, model/tool choice, integration points, controls, monitoring needs and expected operational ownership.
• Recommend the appropriate solution path across multi-stack options such as Microsoft Copilot / Copilot Studio, Azure OpenAI, OpenAI APIs, AWS Bedrock, internal platforms, vendor tools, workflow automation, OCR/document AI, data pipelines or traditional automation.
• Define prototype architecture for solutions such as document summarization, classification, extraction, assisted research, SOP automation, knowledge assistants, workflow triage, QA automation, report generation and decision-support tools.
• Collaborate with technology, security, data, enterprise architecture and platform teams to validate feasibility and ensure solution alignment with enterprise standards.

C. Hands-on MVP Solution Development and Delivery Ownership

• Build or co-build working prototypes and MVP solutions to validate assumptions before large-scale investment.
• Create prompt libraries, structured prompt workflows, lightweight agents, retrieval-enabled assistants, automation flows, simple front-end interfaces, evaluation datasets and testing scripts as appropriate.
• Use practical development tools such as Python, APIs, low-code platforms, automation tools, cloud AI services and enterprise AI platforms to demonstrate feasibility.
• Own selected AI initiatives from discovery through prototype, pilot and implementation handoff, ensuring clear scope, milestones, dependencies, risks and stakeholder decisions.
• Coordinate with business sponsors, AI Champions, product/technology teams, governance reviewers and external partners to remove blockers and maintain momentum.

D. AI Enablement, and Responsible AI

• Coach teams on use-case framing, prompt design, responsible experimentation, and impact measurement.
• Create reusable assets such as use-case canvas templates, prompt libraries, architecture patterns, RAG design checklists, evaluation rubrics, business case templates  and governance checklists
• Embed responsible AI principles into solution design, including human oversight, explainability where appropriate, data minimization, privacy, security, fairness, quality evaluation and escalation paths.
• Identify potential risks such as sensitive data exposure, hallucination, over-automation, insufficient human review, regulatory constraints, intellectual property concerns, model drift or poor user adoption.

Required Academic and Professional Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, Statistics, Mathematics, or a related quantitative/technical discipline.
• Preferred: MBA, postgraduate degree or executive education in AI/ML.
• Preferred certifications: Microsoft Azure AI Engineer / Azure Solutions Architect, Google Cloud AI/ML certifications, or recognized GenAI / responsible AI credentials.
• 8–12 years of professional experience in AI solution engineering and transformation, digital transformation, solution architecture, consulting, intelligent automation
• At least 3 years of experience working on AI, analytics, automation, data-driven or digital solution initiatives where technology was used to solve business workflow problems.
• Demonstrated hands-on exposure to building or co-building AI solutions, prototypes, MVPs, automations, AI assistants, analytics tools, workflow applications or data-driven solutions.

Experience in a GCC or financial services or consulting environment is strongly preferred
Competency Area and Required Capabilities

• AI and GenAI fluency - LLMs, GenAI, prompt engineering, RAG, agents, model evaluation, AI risks ​
• Architecture thinking  - Data flow, integration, APIs, cloud AI services, security and deployment considerations

• Hands-on development - Python, APIs, automation tools, low-code tools, AI platforms, testing approaches.

• Business consulting - Problem framing, value sizing, stakeholder interviews, prioritization, business cases.

• Delivery leadership - Scope management, dependency tracking, implementation handoff, adoption planning .

• Governance mindset - Privacy, security, responsible AI, risk controls, human-in-loop design .

• Communication  - Executive storytelling, workshop facilitation, clear documentation

Morningstar is an equal opportunity employer.

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.